1. Field
At least one example embodiment relates to an object recognition method and apparatus, and a recognizer learning method and apparatus.
2. Description of the Related Art
In a transition to the 21st century information society, information on particular organizations and personal information may have significant importance. To protect the above important information, various types of passwords are used, and other technologies for verifying identity are desperately desired. Among these technologies, face recognition technologies have been evaluated as the most convenient and competitive identity verification methods because a user does not need to take a particular motion or action, and moreover a user's identity can be verified while the user does not recognize it.
Currently, a face recognition technology including recognizing a face by applying a principal component analysis (PCA) to a face image is being used frequently. The PCA refers to a scheme of projecting image data onto a low-dimensional eigenvector space while reducing or, alternatively, minimizing a loss of unique information of an image so as to reduce information. A method of extracting a principal feature vector of a face and recognizing the face using a pattern classifier learned using a principal component vector extracted from a preregistered image has been used frequently as a face recognition method using the PCA. However, by using the method to recognize a face with a large amount of information, a recognition speed and reliability may be reduced, and a satisfactory face recognition result may not be obtained in a change in a pose or a facial expression, even though a feature robust against illumination is obtained based on a selection of a PCA basis vector.
A face recognition performance varies depending on a performance of a pattern classifier to distinguish a registered face from an unregistered face. To learn the pattern classifier, an artificial neural network may be used.